Speaker
Description
In an online learning environment where students do not have direct face-to-face interactions with instructors, observing their learning behaviors is a challenge. As education shifts from traditional to virtual classrooms, it is crucial to explore methods that provide a deeper understanding of student participation in an online learning setting, especially in the Philippine context, where it is relatively very scarce or not investigated at all. This study applied a data-driven approach using data mining techniques to analyze the access patterns and engagement of online students in Technology in ODel Massive Open Online Course (MOOC) at an open university in the Philippines. Data were collected from an open-source learning management system (Moodle) using selected standard report plugins. Descriptive and inferential statistics, clustering (K-means algorithm), and visualization techniques were employed to examine behavioral patterns and preferences, including login frequency, access to course materials, posts and views in discussion forums, and submission and completion of assignments and quizzes. Findings provide insights into learner behaviors and suggest strategies for enhancing engagement and instructional design in MOOCs.
| 発表日の希望 / Preferred Day | 2月28日（土）/ February 28 Saturday |
|---|---|
| MAJ R&D Grant | いいえ |
